Overview

Brought to you by YData

Dataset statistics

Number of variables8
Number of observations600529
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory41.2 MiB
Average record size in memory72.0 B

Variable types

Numeric2
Categorical6

Alerts

labour_force is highly overall correlated with uomHigh correlation
uom is highly overall correlated with labour_forceHigh correlation

Reproduction

Analysis started2024-10-09 17:19:08.834245
Analysis finished2024-10-09 17:19:20.527071
Duration11.69 seconds
Software versionydata-profiling vv4.10.0
Download configurationconfig.json

Variables

ref_date
Real number (ℝ)

Distinct48
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2000.7334
Minimum1976
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.2 MiB
2024-10-09T13:19:20.637822image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1976
5-th percentile1979
Q11990
median2001
Q32012
95-th percentile2021
Maximum2023
Range47
Interquartile range (IQR)22

Descriptive statistics

Standard deviation13.343923
Coefficient of variation (CV)0.0066695157
Kurtosis-1.1152644
Mean2000.7334
Median Absolute Deviation (MAD)11
Skewness-0.080163996
Sum1.2014984 × 109
Variance178.06027
MonotonicityIncreasing
2024-10-09T13:19:20.794925image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
2020 13891
 
2.3%
1992 13758
 
2.3%
1993 13697
 
2.3%
2009 13669
 
2.3%
1991 13651
 
2.3%
2010 13587
 
2.3%
1994 13577
 
2.3%
2012 13498
 
2.2%
1987 13496
 
2.2%
2011 13447
 
2.2%
Other values (38) 464258
77.3%
ValueCountFrequency (%)
1976 9158
1.5%
1977 9325
1.6%
1978 9423
1.6%
1979 9377
1.6%
1980 9311
1.6%
1981 9403
1.6%
1982 9814
1.6%
1983 9957
1.7%
1984 9990
1.7%
1985 10030
1.7%
ValueCountFrequency (%)
2023 12924
2.2%
2022 12834
2.1%
2021 13254
2.2%
2020 13891
2.3%
2019 13125
2.2%
2018 13127
2.2%
2017 13239
2.2%
2016 13418
2.2%
2015 13382
2.2%
2014 13343
2.2%

geo
Categorical

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.2 MiB
Nova Scotia
60489 
Manitoba
59622 
Saskatchewan
57442 
New Brunswick
57416 
British Columbia
55349 
Other values (6)
310211 

Length

Max length25
Median length13
Mean length11.892658
Min length6

Characters and Unicode

Total characters7141886
Distinct characters32
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCanada
2nd rowCanada
3rd rowCanada
4th rowCanada
5th rowCanada

Common Values

ValueCountFrequency (%)
Nova Scotia 60489
10.1%
Manitoba 59622
9.9%
Saskatchewan 57442
9.6%
New Brunswick 57416
9.6%
British Columbia 55349
9.2%
Quebec 54089
9.0%
Alberta 52523
8.7%
Prince Edward Island 52245
8.7%
Newfoundland and Labrador 51682
8.6%
Ontario 51058
8.5%

Length

2024-10-09T13:19:20.948536image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nova 60489
 
6.2%
scotia 60489
 
6.2%
manitoba 59622
 
6.1%
saskatchewan 57442
 
5.9%
new 57416
 
5.8%
brunswick 57416
 
5.8%
british 55349
 
5.6%
columbia 55349
 
5.6%
quebec 54089
 
5.5%
alberta 52523
 
5.4%
Other values (8) 411453
41.9%

Most occurring characters

ValueCountFrequency (%)
a 1028538
14.4%
n 533688
 
7.5%
i 446877
 
6.3%
r 424200
 
5.9%
d 412077
 
5.8%
o 390371
 
5.5%
381108
 
5.3%
e 379486
 
5.3%
t 336483
 
4.7%
c 281681
 
3.9%
Other values (22) 2527377
35.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7141886
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1028538
14.4%
n 533688
 
7.5%
i 446877
 
6.3%
r 424200
 
5.9%
d 412077
 
5.8%
o 390371
 
5.5%
381108
 
5.3%
e 379486
 
5.3%
t 336483
 
4.7%
c 281681
 
3.9%
Other values (22) 2527377
35.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7141886
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1028538
14.4%
n 533688
 
7.5%
i 446877
 
6.3%
r 424200
 
5.9%
d 412077
 
5.8%
o 390371
 
5.5%
381108
 
5.3%
e 379486
 
5.3%
t 336483
 
4.7%
c 281681
 
3.9%
Other values (22) 2527377
35.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7141886
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1028538
14.4%
n 533688
 
7.5%
i 446877
 
6.3%
r 424200
 
5.9%
d 412077
 
5.8%
o 390371
 
5.5%
381108
 
5.3%
e 379486
 
5.3%
t 336483
 
4.7%
c 281681
 
3.9%
Other values (22) 2527377
35.4%

labour_force
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.2 MiB
Labour force
119629 
Full-time employment
114811 
Employment
114143 
Part-time employment
90265 
Unemployment
81821 

Length

Max length20
Median length17
Mean length15.01671
Min length10

Characters and Unicode

Total characters9017970
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLabour force
2nd rowLabour force
3rd rowLabour force
4th rowLabour force
5th rowLabour force

Common Values

ValueCountFrequency (%)
Labour force 119629
19.9%
Full-time employment 114811
19.1%
Employment 114143
19.0%
Part-time employment 90265
15.0%
Unemployment 81821
13.6%
Unemployment rate 79860
13.3%

Length

2024-10-09T13:19:21.087683image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-09T13:19:21.214857image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
employment 319219
31.8%
unemployment 161681
16.1%
labour 119629
 
11.9%
force 119629
 
11.9%
full-time 114811
 
11.4%
part-time 90265
 
9.0%
rate 79860
 
7.9%

Most occurring characters

ValueCountFrequency (%)
e 1252222
13.9%
m 1166876
12.9%
t 856101
9.5%
o 720158
 
8.0%
l 710522
 
7.9%
n 642581
 
7.1%
y 480900
 
5.3%
p 480900
 
5.3%
r 409383
 
4.5%
404565
 
4.5%
Other values (12) 1893762
21.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9017970
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1252222
13.9%
m 1166876
12.9%
t 856101
9.5%
o 720158
 
8.0%
l 710522
 
7.9%
n 642581
 
7.1%
y 480900
 
5.3%
p 480900
 
5.3%
r 409383
 
4.5%
404565
 
4.5%
Other values (12) 1893762
21.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9017970
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1252222
13.9%
m 1166876
12.9%
t 856101
9.5%
o 720158
 
8.0%
l 710522
 
7.9%
n 642581
 
7.1%
y 480900
 
5.3%
p 480900
 
5.3%
r 409383
 
4.5%
404565
 
4.5%
Other values (12) 1893762
21.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9017970
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1252222
13.9%
m 1166876
12.9%
t 856101
9.5%
o 720158
 
8.0%
l 710522
 
7.9%
n 642581
 
7.1%
y 480900
 
5.3%
p 480900
 
5.3%
r 409383
 
4.5%
404565
 
4.5%
Other values (12) 1893762
21.0%
Distinct29
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.2 MiB
Accommodation and food services [72]
 
28627
Wholesale and retail trade [41, 44-45]
 
27891
Goods-producing sector
 
27561
Other services (except public administration) [81]
 
27035
Manufacturing [31-33]
 
26169
Other values (24)
463246 

Length

Max length75
Median length52
Mean length35.70306
Min length14

Characters and Unicode

Total characters21440723
Distinct characters58
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGoods-producing sector
2nd rowAgriculture [111-112, 1100, 1151-1152]
3rd rowAgriculture [111-112, 1100, 1151-1152]
4th rowForestry, fishing, mining, quarrying, oil and gas [21, 113-114, 1153, 2100]
5th rowForestry, fishing, mining, quarrying, oil and gas [21, 113-114, 1153, 2100]

Common Values

ValueCountFrequency (%)
Accommodation and food services [72] 28627
 
4.8%
Wholesale and retail trade [41, 44-45] 27891
 
4.6%
Goods-producing sector 27561
 
4.6%
Other services (except public administration) [81] 27035
 
4.5%
Manufacturing [31-33] 26169
 
4.4%
Information, culture and recreation [51, 71] 26011
 
4.3%
Business, building and other support services [55, 56] 25823
 
4.3%
Construction [23] 25561
 
4.3%
Educational services [61] 25348
 
4.2%
Health care and social assistance [62] 24919
 
4.1%
Other values (19) 335584
55.9%

Length

2024-10-09T13:19:21.380444image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
and 316801
 
11.1%
services 128979
 
4.5%
trade 65963
 
2.3%
other 52858
 
1.9%
administration 51763
 
1.8%
public 51763
 
1.8%
sector 50046
 
1.8%
retail 49570
 
1.7%
44-45 49570
 
1.7%
wholesale 44284
 
1.6%
Other values (80) 1980604
69.7%

Most occurring characters

ValueCountFrequency (%)
2241672
 
10.5%
a 1411872
 
6.6%
i 1408338
 
6.6%
n 1377195
 
6.4%
e 1368236
 
6.4%
s 1159740
 
5.4%
r 1145025
 
5.3%
t 1041735
 
4.9%
o 893175
 
4.2%
1 823852
 
3.8%
Other values (48) 8569883
40.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 21440723
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2241672
 
10.5%
a 1411872
 
6.6%
i 1408338
 
6.6%
n 1377195
 
6.4%
e 1368236
 
6.4%
s 1159740
 
5.4%
r 1145025
 
5.3%
t 1041735
 
4.9%
o 893175
 
4.2%
1 823852
 
3.8%
Other values (48) 8569883
40.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 21440723
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2241672
 
10.5%
a 1411872
 
6.6%
i 1408338
 
6.6%
n 1377195
 
6.4%
e 1368236
 
6.4%
s 1159740
 
5.4%
r 1145025
 
5.3%
t 1041735
 
4.9%
o 893175
 
4.2%
1 823852
 
3.8%
Other values (48) 8569883
40.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 21440723
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2241672
 
10.5%
a 1411872
 
6.6%
i 1408338
 
6.6%
n 1377195
 
6.4%
e 1368236
 
6.4%
s 1159740
 
5.4%
r 1145025
 
5.3%
t 1041735
 
4.9%
o 893175
 
4.2%
1 823852
 
3.8%
Other values (48) 8569883
40.0%

sex
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.2 MiB
Both sexes
216457 
Males
201342 
Females
182730 

Length

Max length10
Median length7
Mean length7.4107828
Min length5

Characters and Unicode

Total characters4450390
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemales
2nd rowFemales
3rd rowFemales
4th rowBoth sexes
5th rowBoth sexes

Common Values

ValueCountFrequency (%)
Both sexes 216457
36.0%
Males 201342
33.5%
Females 182730
30.4%

Length

2024-10-09T13:19:21.506803image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-09T13:19:21.613030image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
both 216457
26.5%
sexes 216457
26.5%
males 201342
24.6%
females 182730
22.4%

Most occurring characters

ValueCountFrequency (%)
e 999716
22.5%
s 816986
18.4%
a 384072
 
8.6%
l 384072
 
8.6%
B 216457
 
4.9%
o 216457
 
4.9%
t 216457
 
4.9%
h 216457
 
4.9%
216457
 
4.9%
x 216457
 
4.9%
Other values (3) 566802
12.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4450390
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 999716
22.5%
s 816986
18.4%
a 384072
 
8.6%
l 384072
 
8.6%
B 216457
 
4.9%
o 216457
 
4.9%
t 216457
 
4.9%
h 216457
 
4.9%
216457
 
4.9%
x 216457
 
4.9%
Other values (3) 566802
12.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4450390
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 999716
22.5%
s 816986
18.4%
a 384072
 
8.6%
l 384072
 
8.6%
B 216457
 
4.9%
o 216457
 
4.9%
t 216457
 
4.9%
h 216457
 
4.9%
216457
 
4.9%
x 216457
 
4.9%
Other values (3) 566802
12.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4450390
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 999716
22.5%
s 816986
18.4%
a 384072
 
8.6%
l 384072
 
8.6%
B 216457
 
4.9%
o 216457
 
4.9%
t 216457
 
4.9%
h 216457
 
4.9%
216457
 
4.9%
x 216457
 
4.9%
Other values (3) 566802
12.7%

age_group
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.2 MiB
15 years and over
167650 
25 to 54 years
159332 
15 to 24 years
144270 
55 years and over
129277 

Length

Max length17
Median length14
Mean length15.483327
Min length14

Characters and Unicode

Total characters9298187
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row55 years and over
2nd row15 to 24 years
3rd row55 years and over
4th row15 to 24 years
5th row55 years and over

Common Values

ValueCountFrequency (%)
15 years and over 167650
27.9%
25 to 54 years 159332
26.5%
15 to 24 years 144270
24.0%
55 years and over 129277
21.5%

Length

2024-10-09T13:19:21.741264image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-09T13:19:21.862454image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
years 600529
25.0%
15 311920
13.0%
to 303602
12.6%
and 296927
12.4%
over 296927
12.4%
25 159332
 
6.6%
54 159332
 
6.6%
24 144270
 
6.0%
55 129277
 
5.4%

Most occurring characters

ValueCountFrequency (%)
1801587
19.4%
e 897456
9.7%
a 897456
9.7%
r 897456
9.7%
5 889138
9.6%
y 600529
 
6.5%
s 600529
 
6.5%
o 600529
 
6.5%
1 311920
 
3.4%
2 303602
 
3.3%
Other values (5) 1497985
16.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9298187
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1801587
19.4%
e 897456
9.7%
a 897456
9.7%
r 897456
9.7%
5 889138
9.6%
y 600529
 
6.5%
s 600529
 
6.5%
o 600529
 
6.5%
1 311920
 
3.4%
2 303602
 
3.3%
Other values (5) 1497985
16.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9298187
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1801587
19.4%
e 897456
9.7%
a 897456
9.7%
r 897456
9.7%
5 889138
9.6%
y 600529
 
6.5%
s 600529
 
6.5%
o 600529
 
6.5%
1 311920
 
3.4%
2 303602
 
3.3%
Other values (5) 1497985
16.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9298187
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1801587
19.4%
e 897456
9.7%
a 897456
9.7%
r 897456
9.7%
5 889138
9.6%
y 600529
 
6.5%
s 600529
 
6.5%
o 600529
 
6.5%
1 311920
 
3.4%
2 303602
 
3.3%
Other values (5) 1497985
16.1%

uom
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.2 MiB
Persons
520669 
Percentage
79860 

Length

Max length10
Median length7
Mean length7.3989483
Min length7

Characters and Unicode

Total characters4443283
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPersons
2nd rowPersons
3rd rowPersons
4th rowPersons
5th rowPersons

Common Values

ValueCountFrequency (%)
Persons 520669
86.7%
Percentage 79860
 
13.3%

Length

2024-10-09T13:19:22.002107image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-09T13:19:22.113319image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
persons 520669
86.7%
percentage 79860
 
13.3%

Most occurring characters

ValueCountFrequency (%)
s 1041338
23.4%
e 760249
17.1%
P 600529
13.5%
r 600529
13.5%
n 600529
13.5%
o 520669
11.7%
c 79860
 
1.8%
t 79860
 
1.8%
a 79860
 
1.8%
g 79860
 
1.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4443283
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 1041338
23.4%
e 760249
17.1%
P 600529
13.5%
r 600529
13.5%
n 600529
13.5%
o 520669
11.7%
c 79860
 
1.8%
t 79860
 
1.8%
a 79860
 
1.8%
g 79860
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4443283
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 1041338
23.4%
e 760249
17.1%
P 600529
13.5%
r 600529
13.5%
n 600529
13.5%
o 520669
11.7%
c 79860
 
1.8%
t 79860
 
1.8%
a 79860
 
1.8%
g 79860
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4443283
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 1041338
23.4%
e 760249
17.1%
P 600529
13.5%
r 600529
13.5%
n 600529
13.5%
o 520669
11.7%
c 79860
 
1.8%
t 79860
 
1.8%
a 79860
 
1.8%
g 79860
 
1.8%

value
Real number (ℝ)

Distinct702
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.644958
Minimum0.2
Maximum70.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.2 MiB
2024-10-09T13:19:22.239041image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile0.6
Q12.1
median5.8
Q314.9
95-th percentile45.8
Maximum70.3
Range70.1
Interquartile range (IQR)12.8

Descriptive statistics

Standard deviation14.443319
Coefficient of variation (CV)1.2403067
Kurtosis3.4630232
Mean11.644958
Median Absolute Deviation (MAD)4.5
Skewness1.9515623
Sum6993134.8
Variance208.60948
MonotonicityNot monotonic
2024-10-09T13:19:22.390149image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5 14166
 
2.4%
0.6 12044
 
2.0%
0.7 10663
 
1.8%
0.8 9552
 
1.6%
0.9 8528
 
1.4%
1 7758
 
1.3%
1.5 7722
 
1.3%
1.6 7555
 
1.3%
0.2 7453
 
1.2%
1.1 7183
 
1.2%
Other values (692) 507905
84.6%
ValueCountFrequency (%)
0.2 7453
1.2%
0.3 4960
 
0.8%
0.4 3421
 
0.6%
0.5 14166
2.4%
0.6 12044
2.0%
0.7 10663
1.8%
0.8 9552
1.6%
0.9 8528
1.4%
1 7758
1.3%
1.1 7183
1.2%
ValueCountFrequency (%)
70.3 86
< 0.1%
70.2 89
< 0.1%
70.1 90
< 0.1%
70 106
< 0.1%
69.9 101
< 0.1%
69.8 88
< 0.1%
69.7 85
< 0.1%
69.6 103
< 0.1%
69.5 94
< 0.1%
69.4 102
< 0.1%

Interactions

2024-10-09T13:19:19.147785image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T13:19:16.154192image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T13:19:19.305877image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-10-09T13:19:17.221691image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2024-10-09T13:19:22.488498image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
age_groupgeolabour_forcenorth_american_industry_classification_system_(naics)ref_datesexuomvalue
age_group1.0000.0780.1100.0890.0360.0340.1190.068
geo0.0781.0000.1070.0720.0070.0440.1420.115
labour_force0.1100.1071.0000.1550.0100.0601.0000.111
north_american_industry_classification_system_(naics)0.0890.0720.1551.0000.0790.0860.1760.068
ref_date0.0360.0070.0100.0791.0000.0060.014-0.010
sex0.0340.0440.0600.0860.0061.0000.0500.041
uom0.1190.1421.0000.1760.0140.0501.0000.183
value0.0680.1150.1110.068-0.0100.0410.1831.000

Missing values

2024-10-09T13:19:19.532840image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-09T13:19:19.902488image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

ref_dategeolabour_forcenorth_american_industry_classification_system_(naics)sexage_groupuomvalue
231976CanadaLabour forceGoods-producing sectorFemales55 years and overPersons59.2
331976CanadaLabour forceAgriculture [111-112, 1100, 1151-1152]Females15 to 24 yearsPersons25.0
351976CanadaLabour forceAgriculture [111-112, 1100, 1151-1152]Females55 years and overPersons16.2
371976CanadaLabour forceForestry, fishing, mining, quarrying, oil and gas [21, 113-114, 1153, 2100]Both sexes15 to 24 yearsPersons66.5
391976CanadaLabour forceForestry, fishing, mining, quarrying, oil and gas [21, 113-114, 1153, 2100]Both sexes55 years and overPersons27.9
411976CanadaLabour forceForestry, fishing, mining, quarrying, oil and gas [21, 113-114, 1153, 2100]Males15 to 24 yearsPersons59.7
431976CanadaLabour forceForestry, fishing, mining, quarrying, oil and gas [21, 113-114, 1153, 2100]Males55 years and overPersons26.7
441976CanadaLabour forceForestry, fishing, mining, quarrying, oil and gas [21, 113-114, 1153, 2100]Females15 years and overPersons19.6
451976CanadaLabour forceForestry, fishing, mining, quarrying, oil and gas [21, 113-114, 1153, 2100]Females15 to 24 yearsPersons6.7
461976CanadaLabour forceForestry, fishing, mining, quarrying, oil and gas [21, 113-114, 1153, 2100]Females25 to 54 yearsPersons11.7
ref_dategeolabour_forcenorth_american_industry_classification_system_(naics)sexage_groupuomvalue
9986782023British ColumbiaUnemployment rateAccommodation and food services [72]Both sexes25 to 54 yearsPercentage3.8
9986802023British ColumbiaUnemployment rateAccommodation and food services [72]Males15 years and overPercentage5.4
9986812023British ColumbiaUnemployment rateAccommodation and food services [72]Males15 to 24 yearsPercentage5.8
9986822023British ColumbiaUnemployment rateAccommodation and food services [72]Males25 to 54 yearsPercentage5.3
9986842023British ColumbiaUnemployment rateAccommodation and food services [72]Females15 years and overPercentage3.0
9986852023British ColumbiaUnemployment rateAccommodation and food services [72]Females15 to 24 yearsPercentage3.7
9986882023British ColumbiaUnemployment rateOther services (except public administration) [81]Both sexes15 years and overPercentage2.8
9986922023British ColumbiaUnemployment rateOther services (except public administration) [81]Males15 years and overPercentage3.2
9986962023British ColumbiaUnemployment rateOther services (except public administration) [81]Females15 years and overPercentage2.5
9987002023British ColumbiaUnemployment ratePublic administration [91]Both sexes15 years and overPercentage1.3